scholarly journals Detection of Epistatic and Gene-Environment Interactions Underlying Three Quality Traits in Rice Using High-Throughput Genome-Wide Data

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Haiming Xu ◽  
Beibei Jiang ◽  
Yujie Cao ◽  
Yingxin Zhang ◽  
Xiaodeng Zhan ◽  
...  
Keyword(s):  
Linear Model ◽  
Complex Traits ◽  
Genetic Architecture ◽  
Mixed Linear Model ◽  
Processing Unit ◽  
Environment Interaction ◽  
Quality Traits ◽  
Gene Environment ◽  
Genome Wide ◽  
Model Approach

With development of sequencing technology, dense single nucleotide polymorphisms (SNPs) have been available, enabling uncovering genetic architecture of complex traits by genome-wide association study (GWAS). However, the current GWAS strategy usually ignores epistatic and gene-environment interactions due to absence of appropriate methodology and heavy computational burden. This study proposed a new GWAS strategy by combining the graphics processing unit- (GPU-) based generalized multifactor dimensionality reduction (GMDR) algorithm with mixed linear model approach. The reliability and efficiency of the analytical methods were verified through Monte Carlo simulations, suggesting that a population size of nearly 150 recombinant inbred lines (RILs) had a reasonable resolution for the scenarios considered. Further, a GWAS was conducted with the above two-step strategy to investigate the additive, epistatic, and gene-environment associations between 701,867 SNPs and three important quality traits, gelatinization temperature, amylose content, and gel consistency, in a RIL population with 138 individuals derived from super-hybrid rice Xieyou9308 in two environments. Four significant SNPs were identified with additive, epistatic, and gene-environment interaction effects. Our study showed that the mixed linear model approach combining with the GPU-based GMDR algorithm is a feasible strategy for implementing GWAS to uncover genetic architecture of crop complex traits.

scholarly journals Mixed linear model approach adapted for genome-wide association studies

2010 ◽  
Vol 42 (4) ◽  
pp. 355-360 ◽  
Author(s):  
Zhiwu Zhang ◽  
Elhan Ersoz ◽  
Chao-Qiang Lai ◽  
Rory J Todhunter ◽  
Hemant K Tiwari ◽  
...  
Keyword(s):  
Linear Model ◽  
Association Studies ◽  
Mixed Linear Model ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Model Approach

scholarly journals Population-specific causal disease effect sizes in functionally important regions impacted by selection

2019 ◽  
Author(s):  
Huwenbo Shi ◽  
Steven Gazal ◽  
Masahiro Kanai ◽  
Evan M. Koch ◽  
Armin P. Schoech ◽  
...  
Keyword(s):  
Genetic Correlation ◽  
Complex Traits ◽  
Causal Effect ◽  
Genetic Correlations ◽  
Effect Sizes ◽  
Environment Interaction ◽  
Immune Genes ◽  
Gene Environment ◽  
Genome Wide ◽  
Disease Effect

AbstractMany diseases and complex traits exhibit population-specific causal effect sizes with trans-ethnic genetic correlations significantly less than 1, limiting trans-ethnic polygenic risk prediction. We developed a new method, S-LDXR, for stratifying squared trans-ethnic genetic correlation across genomic annotations, and applied S-LDXR to genome-wide association summary statistics for 31 diseases and complex traits in East Asians (EAS) and Europeans (EUR) (average NEAS=90K, NEUR=267K) with an average trans-ethnic genetic correlation of 0.85 (s.e. 0.01). We determined that squared trans-ethnic genetic correlation was 0.82× (s.e. 0.01) smaller than the genome-wide average at SNPs in the top quintile of background selection statistic, implying more population-specific causal effect sizes. Accordingly, causal effect sizes were more population-specific in functionally important regions, including conserved and regulatory regions. In analyses of regions surrounding specifically expressed genes, causal effect sizes were most population-specific for skin and immune genes and least population-specific for brain genes. Our results could potentially be explained by stronger gene-environment interaction at loci impacted by selection, particularly positive selection.

scholarly journals The PAX1 locus at 20p11 is a modifier for bilateral cleft lip only

2020 ◽  
Author(s):  
Sarah W. Curtis ◽  
Daniel Chang ◽  
Myoung Keun Lee ◽  
John R. Shaffer ◽  
Karlijne Indencleef ◽  
...  
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Cleft Lip ◽  
Mixed Model ◽  
Cleft Lip And Palate ◽  
Phenotypic Heterogeneity ◽  
Mixed Model Approach ◽  
Genome Wide ◽  
A Genome ◽  
Model Approach

AbstractNonsyndromic orofacial clefts (OFCs) are the most common craniofacial birth defect in humans and, like many complex traits, OFCs are phenotypically and etiologically heterogenous. The phenotypic heterogeneity of OFCs extends beyond the structures affected by the cleft (e.g., cleft lip (CL) and cleft lip and palate (CLP) to other features, such as the severity of the cleft. Here, we focus on bilateral and unilateral clefts as one dimension of OFC severity. Unilateral clefts are more frequent than bilateral clefts for both CL and CLP, but the genetic architecture of these subtypes is not well understood, and it is not known if genetic variants predispose for the formation of one subtype over another. Therefore, we tested for subtype-specific genetic associations in 44 bilateral CL (BCL) cases, 434 unilateral CL (UCL) cases, 530 bilateral CLP cases (BCLP), 1123 unilateral CLP (UCLP) cases, and unrelated controls (N = 1626), using the mixed-model approach implemented in GENESIS. While no novel loci were found in subtype-specific analyses comparing cases to controls, the genetic architecture of UCL was distinct compared to BCL, with 43.8% of suggestive loci (p < 1.0×10−5) having non-overlapping confidence intervals between the two subtypes. To further understand the genetic risk factors for severity differences, we then performed a genome-wide scan for modifiers using a similar mixed-model approach and found one genome-wide significant modifier locus on 20p11 (p = 7.53×10−9), 300kb downstream of PAX1, associated with higher odds of BCL compared to UCL, which also replicated in an independent cohort (p = 0.0018) and showed no effect in BCLP (p>0.05). We further found that SNPs at this locus were associated with normal human nasal shape. Taken together, these results suggest bilateral and unilateral clefts may have differences in their genetic architecture, especially between CL and CLP. Moreover, our results suggest BCL, the rarest form of OFC, may be genetically distinct from the other OFC subtypes. This expands our understanding of genetic modifiers for subtypes of OFCs and further elucidates the genetic mechanisms behind the phenotypic heterogeneity in OFCs.

scholarly journals Population-specific causal disease effect sizes in functionally important regions impacted by selection

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Huwenbo Shi ◽  
Steven Gazal ◽  
Masahiro Kanai ◽  
Evan M. Koch ◽  
Armin P. Schoech ◽  
...  
Keyword(s):  
Genetic Correlation ◽  
Complex Traits ◽  
Causal Effect ◽  
Genetic Correlations ◽  
Effect Sizes ◽  
Environment Interaction ◽  
Immune Genes ◽  
Gene Environment ◽  
Genome Wide ◽  
Disease Effect

AbstractMany diseases exhibit population-specific causal effect sizes with trans-ethnic genetic correlations significantly less than 1, limiting trans-ethnic polygenic risk prediction. We develop a new method, S-LDXR, for stratifying squared trans-ethnic genetic correlation across genomic annotations, and apply S-LDXR to genome-wide summary statistics for 31 diseases and complex traits in East Asians (average N = 90K) and Europeans (average N = 267K) with an average trans-ethnic genetic correlation of 0.85. We determine that squared trans-ethnic genetic correlation is 0.82× (s.e. 0.01) depleted in the top quintile of background selection statistic, implying more population-specific causal effect sizes. Accordingly, causal effect sizes are more population-specific in functionally important regions, including conserved and regulatory regions. In regions surrounding specifically expressed genes, causal effect sizes are most population-specific for skin and immune genes, and least population-specific for brain genes. Our results could potentially be explained by stronger gene-environment interaction at loci impacted by selection, particularly positive selection.

scholarly journals Family-based gene-environment interaction using sequence kernel association test (FGE-SKAT) for complex quantitative traits

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chao-Yu Guo ◽  
Reng-Hong Wang ◽  
Hsin-Chou Yang
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
Association Test ◽  
Whole Genome Sequence ◽  
Environment Interaction ◽  
Genome Wide Association Studies ◽  
Whole Genome ◽  
Sequence Kernel Association Test ◽  
Gene Environment ◽  
Family Based

AbstractAfter the genome-wide association studies (GWAS) era, whole-genome sequencing is highly engaged in identifying the association of complex traits with rare variations. A score-based variance-component test has been proposed to identify common and rare genetic variants associated with complex traits while quickly adjusting for covariates. Such kernel score statistic allows for familial dependencies and adjusts for random confounding effects. However, the etiology of complex traits may involve the effects of genetic and environmental factors and the complex interactions between genes and the environment. Therefore, in this research, a novel method is proposed to detect gene and gene-environment interactions in a complex family-based association study with various correlated structures. We also developed an R function for the Fast Gene-Environment Sequence Kernel Association Test (FGE-SKAT), which is freely available as supplementary material for easy GWAS implementation to unveil such family-based joint effects. Simulation studies confirmed the validity of the new strategy and the superior statistical power. The FGE-SKAT was applied to the whole genome sequence data provided by Genetic Analysis Workshop 18 (GAW18) and discovered concordant and discordant regions compared to the methods without considering gene by environment interactions.

scholarly journals Genetics of complex traits: prediction of phenotype, identification of causal polymorphisms and genetic architecture

2016 ◽  
Vol 283 (1835) ◽  
pp. 20160569 ◽  
Author(s):  
M. E. Goddard ◽  
K. E. Kemper ◽  
I. M. MacLeod ◽  
A. J. Chamberlain ◽  
B. J. Hayes
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Quantitative Traits ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Crop Breeding ◽  
Single Nucleotide ◽  
Genome Wide ◽  
Phenotype Identification

Complex or quantitative traits are important in medicine, agriculture and evolution, yet, until recently, few of the polymorphisms that cause variation in these traits were known. Genome-wide association studies (GWAS), based on the ability to assay thousands of single nucleotide polymorphisms (SNPs), have revolutionized our understanding of the genetics of complex traits. We advocate the analysis of GWAS data by a statistical method that fits all SNP effects simultaneously, assuming that these effects are drawn from a prior distribution. We illustrate how this method can be used to predict future phenotypes, to map and identify the causal mutations, and to study the genetic architecture of complex traits. The genetic architecture of complex traits is even more complex than previously thought: in almost every trait studied there are thousands of polymorphisms that explain genetic variation. Methods of predicting future phenotypes, collectively known as genomic selection or genomic prediction, have been widely adopted in livestock and crop breeding, leading to increased rates of genetic improvement.

scholarly journals GxEsum: a novel approach to estimate the phenotypic variance explained by genome-wide GxE interaction based on GWAS summary statistics for biobank-scale data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Jisu Shin ◽  
Sang Hong Lee
Keyword(s):  
Complex Traits ◽  
Error Rates ◽  
Type I ◽  
Phenotypic Variance ◽  
Environment Interaction ◽  
Summary Statistics ◽  
Gxe Interaction ◽  
Genome Wide ◽  
Scale Data ◽  
Variance Explained

AbstractGenetic variation in response to the environment, that is, genotype-by-environment interaction (GxE), is fundamental in the biology of complex traits and diseases. However, existing methods are computationally demanding and infeasible to handle biobank-scale data. Here, we introduce GxEsum, a method for estimating the phenotypic variance explained by genome-wide GxE based on GWAS summary statistics. Through comprehensive simulations and analysis of UK Biobank with 288,837 individuals, we show that GxEsum can handle a large-scale biobank dataset with controlled type I error rates and unbiased GxE estimates, and its computational efficiency can be hundreds of times higher than existing GxE methods.

scholarly journals Polygenic interactions with environmental adversity in the aetiology of major depressive disorder

2015 ◽  
Vol 46 (4) ◽  
pp. 759-770 ◽  
Author(s):  
N. Mullins ◽  
R. A. Power ◽  
H. L. Fisher ◽  
K. B. Hanscombe ◽  
J. Euesden ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Environmental Risk ◽  
Complex Traits ◽  
Risk Scores ◽  
Environment Interaction ◽  
Inverse Association ◽  
Major Depressive ◽  
Polygenic Risk ◽  
Gene Environment

BackgroundMajor depressive disorder (MDD) is a common and disabling condition with well-established heritability and environmental risk factors. Gene–environment interaction studies in MDD have typically investigated candidate genes, though the disorder is known to be highly polygenic. This study aims to test for interaction between polygenic risk and stressful life events (SLEs) or childhood trauma (CT) in the aetiology of MDD.MethodThe RADIANT UK sample consists of 1605 MDD cases and 1064 controls with SLE data, and a subset of 240 cases and 272 controls with CT data. Polygenic risk scores (PRS) were constructed using results from a mega-analysis on MDD by the Psychiatric Genomics Consortium. PRS and environmental factors were tested for association with case/control status and for interaction between them.ResultsPRS significantly predicted depression, explaining 1.1% of variance in phenotype (p= 1.9 × 10−6). SLEs and CT were also associated with MDD status (p= 2.19 × 10−4andp= 5.12 × 10−20, respectively). No interactions were found between PRS and SLEs. Significant PRSxCT interactions were found (p= 0.002), but showed an inverse association with MDD status, as cases who experienced more severe CT tended to have a lower PRS than other cases or controls. This relationship between PRS and CT was not observed in independent replication samples.ConclusionsCT is a strong risk factor for MDD but may have greater effect in individuals with lower genetic liability for the disorder. Including environmental risk along with genetics is important in studying the aetiology of MDD and PRS provide a useful approach to investigating gene–environment interactions in complex traits.

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